Strategy Execution Frameworks

Decarbonization Journey 🌎 Effective decarbonization begins with establishing a comprehensive and accurate emissions baseline. This involves measuring direct and indirect emissions using standardized methodologies and ensuring third-party verification to provide transparency and credibility. Without a reliable baseline, it is not possible to track progress or prioritize action effectively. Once emissions are measured, science-based targets must be set to provide direction and accountability. Targets aligned with the 1.5 degree Celsius scenario create a clear benchmark for action and support alignment with international climate commitments. These targets serve as the foundation for long-term planning and investment decisions across business units. Identifying and prioritizing abatement levers is the next critical step. This requires a detailed analysis of emissions hotspots across operations, supply chains, and product lifecycles. Prioritization enables the allocation of resources to the most material reduction opportunities and supports integration into operational planning. With priority areas defined, organizations must build decarbonization pathways that translate targets into practical trajectories. These pathways combine technology options, operational changes, and supplier engagement strategies into structured plans that outline when and how reductions will be achieved over time. Implementation depends on effective resource allocation and internal coordination. Teams must be equipped with the tools, guidance, and incentives to execute the plan. Success also relies on embedding emissions reduction into core decision-making processes, including procurement, logistics, and capital expenditure. Communication plays a critical role in supporting both execution and accountability. Internally, it ensures alignment across departments and leadership. Externally, transparent updates on progress and challenges help build trust among stakeholders, from investors to regulators and customers. Regular disclosure reinforces transparency and continuous improvement. Emissions reporting should follow established frameworks and cover Scope 1, Scope 2, and relevant Scope 3 categories. These disclosures inform stakeholders of current performance and provide a basis for tracking alignment with climate goals. Understanding emission scopes is essential for comprehensive decarbonization. Scope 1 covers direct emissions from owned sources. Scope 2 includes emissions from purchased energy. Scope 3 spans upstream and downstream activities, such as supplier operations, transportation, and product end use. Addressing Scope 3 requires collaboration across the value chain and the integration of sustainability criteria into procurement and product design. Source: Terrascope #sustainability #sustainable #esg #business

𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻 𝗔𝗜 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆? This is one of the clearest roadmap you’ll ever get to build your own: ⬇️ 1. 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗚𝗼𝗮𝗹 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗖𝗼𝗿𝗲): This is your strategic north star — where you define your ambition and guide every downstream decision. • Drivers → Why are you doing this? Clarifies the business/tech forces pushing AI forward.   • Value → What are you aiming to achieve? Links AI directly to measurable outcomes.   • Vision → Where is this going long-term? Provides inspiration and direction across teams.   • Alignment → Is everyone rowing in the same direction? Ensures synergy. • Risks → What could go wrong? Sets the baseline for governance and responsible AI.   • Adoption → Who will actually use it? Anticipates friction and enables change management. 📍 This is the master blueprint — Without this, you’re just building disconnected POCs. No clear target = no impact. 2. 𝗔𝗹𝗶𝗴𝗻𝗲𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗙𝗶𝘁 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀): This is where your AI ambition meets the reality of your broader enterprise. • Business Strategy → AI must serve the core business goals — not exist as a side project.   • IT Strategy → Ensures your infrastructure can support scalable AI.   • R&D Strategy → Aligns innovation with AI capabilities and funding priorities.   • D&A Strategy → Without data strategy, no AI strategy will scale. • (...) Strategy → ... 📍 Connect AI to the real levers of power in your organization — so it doesn’t get siloed or shut down. 3. 𝗔𝗜 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗥𝗲𝗮𝗹):   Once you know what you want to do, this defines how you’ll deliver it at scale. • Governance → Sets up ethical, legal, and operational oversight from day one.   • Data → Builds the pipelines and quality foundations for smart AI.   • Engineering → Equips you with the technical backbone for deployment.   • Technology → Selects the right tools, platforms, and architecture.   • Organization → Assigns ownership and accountability.   • Literacy → Ensures the workforce can actually work with AI. 📍 This is your AI engine room — without it, strategy stays theoretical. 4. 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲):   Now it’s time to build — but with structure and intent. • Ideation/Prioritization** → Surfaces the best use cases, aligned with strategy.   • Use Cases → Translates goals into concrete applications and MVPs.   • Buy-Build → Decides how to deliver: in-house, outsourced, or hybrid.   • Change Management → Drives real adoption beyond pilots.   • Value/Cost Management → Measures success and ensures scalability. 📍 This is where value is realized — where strategy finally touches the customer and the business. 𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗙𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱, 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲! Graphic source: Gartner

Understanding product strategy is foundational to building products that truly deliver value. But I often see teams confuse strategy with roadmaps or a list of features. Strategy isn’t about dictating ‘what’ to build—it’s about building a framework to help the organization decide what to build. Why does this matter? Without a solid product strategy, even the most innovative ideas can lose focus. A strong strategy ensures that every decision your team makes is intentional, helping you navigate complex markets, meet customer demands, and drive meaningful business outcomes. One approach I love for building and refining product strategy is the Product Kata (based on Toyota Kata)—a method that focuses on planning, executing, learning, and iterating to keep your strategy dynamic and adaptive. The Product Kata is built around five essential steps: 1️⃣ Understand the vision: Define what success looks like. Where are you trying to go? This is where you would understand the strategy level above you, make sure it is crystal clear, and identify your business and customer goals. 2️⃣ Assess the current state: Where are you now? Identify gaps and areas that need improvement to move closer to your vision and strategy. This is where we research and gather data to make an informed hypothesis. 3️⃣ Set your next goal: This goal is set from our informed hypothesis, and breaks down the strategy into smaller, achievable goals in the near term. 4️⃣ Experiment and learn: Test your hypotheses with small, targeted experiments. Collect real-world data to understand what works and what doesn’t. 5️⃣ Reflect and iterate: Review the results, compare them to your current state now that you’ve attempted to solve the problem, and adapt based on what you’ve learned. Adjust your goals as you learn new information. What I love about this method is that it prevents strategy from being static. Instead of setting your direction in stone, you’re constantly learning, adjusting, and evolving as you go. A well-executed product strategy bridges the gap between business outcomes and customer value. It keeps teams aligned, focused, and ready to seize opportunities in a rapidly changing landscape. If you’re ready to master this and create strategies that drive results, our Product Strategy course covers this in depth. You’ll learn frameworks like the Product Kata and how to align your team around strategies that deliver real business and customer impact. How do you keep your strategy adaptable and aligned with your vision? I’d love to hear how your team approaches this—share your thoughts in the comments! #productinstitute #productstrategy #productmanagement #leadership #teamalignment #businessgoals #customerfocus #productleaders

I’m in board rooms and executive sessions witnessing AI strategies fall into 3 traps: 1. Too vague (“We need to be more innovative.”) 2. Too detailed (30 page deck with 50 slides in the appendix that no one reads) 3. Too disconnected (Misaligned with actual capabilities) If your AI strategy has more slides than decisions, you might be confusing activity with alignment. The result? ✔️An AI strategy that costs $1M and 75% of the use cases aren’t even executable . ✔️A transformation roadmap that spans 5 years, but no one knows what to do next quarter. AI is not just a tool. It’s a force that can reshape your workflows, redefine roles, and reallocate talent. Without a clear strategy, you’ll fall into two traps: 🤯FOMO-driven chaos: Buying licenses ≠ transformation. 🤯Pilot purgatory: Endless experimentation without scale. But here’s the truth: You don’t need a fancier strategy. You need a functional one. What a Good AI Strategy Actually Needs: 🧭 Clarity – What problem are you solving? – Why AI, not automation or process reengineering? ⚙️ Capability Mapping – Do you have the data? – Do you have the people? – Do you have the infrastructure? 📆 Time-Boxed Roadmap – What’s your “Crawl → Walk → Run” plan over the next 3, 6, 12 months? – How are you measuring success at each step? If your AI strategy doesn’t clearly answer those questions… it’s not a strategy. It’s a slide deck! Sol’s Recommendations: 1️⃣ Think Big. Start Small. Scale Smart. A good strategy should fit on one slide. It should move people to act, not stall them in analysis. 2️⃣ Build Feedback Loops INTO the Strategy Strategy isn’t a map—it’s a GPS. It must update as the terrain shifts. That means monthly retros, live dashboards, and real business input—not just consulting jargon. 3️⃣ Don’t confuse motion with momentum. Start small, but make sure it moves the needle. 4️⃣ Map readiness before roadmap. Strategy isn’t just about what you want to do, it’s about what you’re equipped to do now and how fast you can scale. Great AI strategy isn’t built on use cases but also use-case readiness! What’s the worst strategy deck you’ve ever seen? Drop your horror stories (or recovery stories) below. I’m all ears. #Strategy #Execution #FutureOfWork #AILeadership #DigitalTransformation #SolRashidi #RealTalkStrategy #AI #Automation #Agents #AIstrategy #humanresources

What if we stopped the strategy vs. execution debate and recognized that strategy and execution actually work best in tandem, evolving together. Over and over again, we hear executives talking about the struggle to bridge the gap between strategy formulation and execution, indicating of course that many strategies are not effectively rolled out. 🤷♀️ It has been this way for years and it has taken us too long to realize that traditional set-in-stone strategic plans simply don't work. And neither do execution plans that focus on implementing a predefined strategy. Companies need agile adaptable strategies that respond to real-time challenges. Even if they have a 10 year plan, they still need a REAL-TIME PLAN. It's time to stop viewing strategy as a strict roadmap, and see it as a living framework—something that evolves with our teams, customers, and markets. This way of working requires a mindset of 'doing informs direction' Instead of viewing strategy as a separate, upfront blueprint that’s followed by execution, this approach integrates the two: strategy becomes a fluid process that evolves as teams execute and learn. Traditionalists may struggle with this shift because we are essentially talking about blending strategy and execution from the start- they may even question how to even do it. So, here's a few simple tips: ✳️ 1. Set Up Simple Monitoring and Reporting Systems Instead of waiting for annual reviews, create regular (even monthly) check-ins where teams report on progress and challenges. Encourage them to flag areas where adapting the strategy would be beneficial (means they have to read it regularly). ✳️ 2. Make Updates Part of the Plan: Integrate a simple versioning process ( even quarterly). When adjustments are made, update a “living document” with clear markers noting each update’s rationale and potential impact. This way, everyone works from the same strategic blueprint—just updated as needed. ✳️ 3. Designate Strategy ‘Owners’: Assign individuals or teams as “owners” of specific strategic areas. Their role is to ensure consistency, track changes, and gather insights on what’s working and what needs refinement. This approach makes it easier to manage updates and stay aligned. ✳️ 4. Keep the Big Picture in View: While it’s important to focus on real-time changes, stay connected to your overall goals. Each adjustment should still support the long-term vision. Regularly review how all pieces are coming together. 💡This shift is relevant for every industry, but especially fast-changing industries, where it's clear that waiting for annual reviews or rigid plans has led to missed opportunities for growth and adaptation. ❓ What do you think? Do you agree? _________________________________________ I’m Catherine McDonald, a Lean Business and Leadership Development Coach. Follow me for insights on Lean, Leadership, Coaching, and Organizational Behaviour, or visit my website at  www.mcdconsulting.ie for more information.

“The absence of an accident does not mean the presence of safety.” – A profound statement I lived by during my years spent in industrial safety. Applied to digital transformation, this saying reminds us that just because no immediate issues are surfacing, it doesn’t mean our digital strategies are effective. Just because your initiatives haven’t crashed and burned… Just because your teams are “doing stuff with data”… Just because no one’s raising red flags… Doesn’t mean your strategy is working. In fact, most digital transformations fail not with a bang, but with a shrug. People keep moving, but no one’s aligned. Projects keep happening, but outcomes stay unclear. And eventually, everyone’s left wondering what the point was. 𝐓𝐡𝐞 𝐠𝐨𝐚𝐥 𝐨𝐟 𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲: It’s not about dictating every move your decision-makers should make; it’s about providing them with the guidance they need to make decisions that align with a unified goal. The strategy map isn’t a set of instructions but a compass - it shows the way, focusing on 𝐡𝐨𝐰 to decide rather than 𝐰𝐡𝐚𝐭 to decide. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐯𝐨𝐢𝐝 𝐜𝐡𝐚𝐨𝐬 𝐚𝐧𝐝 𝐜𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧: 𝐌𝐚𝐩 𝐢𝐭: Just like we use maps to navigate unfamiliar roads, employees need a clear, visual guide to understand your digital transformation journey. Build a strategy document that lays out both the destination and key milestones. It should be simple, accessible, and regularly updated to reflect changes — giving everyone a shared view of where you’re going and how to get there. 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐢𝐭: A map only works if people know how to read it. Connect the strategy to daily work by showing how it impacts specific teams, roles, and decisions. Don’t just present the big picture — break it down so people see how their actions contribute to the overall plan. 𝐑𝐞𝐩𝐞𝐚𝐭 𝐢𝐭: Strategy isn’t one-and-done. Reinforce it constantly through meetings, updates, and internal channels. Celebrate aligned actions and share progress often. The goal is to embed the strategy into daily routines until it becomes second nature across the organization. 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e7nH_xhP ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I built the data and AI strategies for some of the world’s most successful businesses. One word helped V Squared beat our Big Consulting competitors to land those clients. Can you guess what it is? Actionable. Strategy must clear the lane for execution and empower decisions. It must serve people who get the job done and deliver results. Most strategies, especially data and AI strategies, create bureaucracy and barriers that slow execution. They paralyze the business, waiting for the perfect conditions and easy opportunities to materialize. CEOs don’t want another slide deck and a confident-sounding presentation about “The AI Opportunity.” They want a pragmatic action plan detailing strategy implementation, execution, delivery, and ROI. They need a framework for budgeting based on multiple versions of the AI product roadmap that quantifies returns at different spending levels. They need frameworks to decide which risks to take. Business units don’t want another lecture about AI literacy. They need a transformation roadmap, a structured learning path, and training resources. They need to know who to bring opportunities to, how to make buying decisions, and when to kick off AI initiatives. Most of all, data and AI strategy must address the messy reality of markets, customers, technical debt, resource constraints, imperfect conditions, and business necessity. Technical strategy is only valuable if it informs decision-making and optimizes actions to achieve the business’s goals.

An observation on (ineffective) safety strategy execution. I've been noticing a pattern (probably formed over the majority of my career but very obvious now I'm consulting)... many safety teams have ambitious, well-considered strategic annual and/or multi-year safety plans - typically comprised of leadership programs, digital transformation roadmap, cultural change initiatives, critical risk programs - but many (most?) struggle to execute on them consistently. Generally speaking I don't think the plans themselves are the problem.. siloed, disconnected plans yes... but for solid plans somewhere between strategy and delivery things stop working. Part of this seems structural (and budget). Safety teams are typically organised around operational delivery - compliance, incident response, auditing, training. These are different capabilities than what's needed to deliver complex transformation programs: portfolio management, stakeholder coordination, phased implementation, benefits tracking. Unlike IT or engineering/operations functions, safety teams rarely have dedicated PMO or transformation office support. They're expected to maintain operational excellence while simultaneously delivering strategic change initiatives, often without the supporting infrastructure other functions take for granted - governance frameworks, resource allocation processes, integrated change management. This becomes especially visible in #SafetyTech initiatives, which aren't simple deployments but complex programs requiring coordination across multiple functions. Without proper program management discipline, strategic vision fragments into disconnected pilots that never scale. I'm curious whether others are seeing/experiencing this too? 🤔Where does execution typically break down in your experience? And... 🤔Are there teams doing this well - if so, what's different about your/their approach? Curious to hear some thoughts 💭 #safetyinnovation #betterworkbydesign

Are you expecting higher performance without redesigning the system that produces it? Fact: Performance pressure has increased. Operating clarity has not. Over the past year, many organisations have reduced headcount while tightening performance expectations. That combination is not neutral. It changes how leadership must operate. What’s failing is not motivation. Not work ethic. Not capability. What’s failing is the operating logic under pressure. Leadership teams are demanding faster execution while keeping the same number of priorities, the same decision bottlenecks, and adding urgency on top of ambiguity. 🔍 The result is predictable: • People expend more effort • Decisions take longer because authority is unclear • Quality declines through rework and risk-avoidance • Critical issues surface late, when options are narrower ❌ This is activity under strain, not performance. The organisations holding up are not pushing harder. They are redesigning how work moves. 👉 If you manage people, lead initiatives, or want to influence change, act on these three points: 1️⃣ Reduce the system’s load Define the two outcomes that matter in the next 30–60 days. Formally pause or stop work that competes with them. Performance improves when capacity matches intent. 2️⃣ Reassign decision rights Identify decisions still escalating by habit rather than risk. Move ownership to the lowest sensible level and make it explicit. Speed follows clarity. 3️⃣ Specify standards, not urgency Replace “as fast as possible” with explicit criteria for quality, scope, and trade-offs. People execute well when success is defined, not when pressure is increased. 📌 This is the leadership work of this moment. Not motivation. Not charisma. Not urgency. Structural clarity under constraint. 🧠 Culture is a critical part of this system work — I’ll address that explicitly in later posts. Before asking for more output, ask: 👉 What ambiguity am I still tolerating in the system I lead? That’s where performance is currently being constrained.

As a journal referee for research papers in finance and investing, I’ve learned that honest authors often make unrealistic assumptions about implementation. For example, they assume portfolio managers can rebalance everything at the closing price of the same day the signal is generated. Or they leave out components of transaction costs, such as the cost of leverage or shorting, the bid-ask spread, or market impact. Worse, some ignore the cost of borrowing and transaction costs altogether. And a more subtle but key caveat is that some strategies do not use budget constraints, such that part of the alpha (outperformance) may come from a systematically long exposure to equity, duration, or other risk premiums versus the static benchmark. Risk-adjusted alphas in research articles should be shaved to account for the inevitable implementation shortfall between backtests and reality. (From the book Beyond Diversification.)